LAUSR

Fraud detection is becoming an integral part of business intelligence, as detecting fraud in the work processes of a company is of great value. Fraud is an inhibitory factor to accurate appraisal in the evaluation of an enterprise, and it is economically a loss factor to business. Previous studies for fraud detection have limited the performance enhancement because they have learned the fraud pattern of the whole data. This paper proposes a novel method using hierarchical clusters based on deep neural networks in order to detect more detailed frauds, as well as frauds of whole data in the work processes of job placement. The proposed method, Hierarchical Clusters-based Deep Neural Networks (HC-DNN) utilizes anomaly characteristics of hierarchical clusters pre-trained through an autoencoder as the initial weights of deep neural networks to detect various frauds. HC-DNN has the advantage of improving the performance and providing the explanation about the relationship of fraud types. As a result of evaluating the performance of fraud detection by cross validation, the results of the proposed method show higher performance than those of conventional methods. And from the viewpoint of explainable deep learning the hierarchical cluster structure constructed through HC-DNN can represent the relationship of fraud types.